People remain accountable
Define who owns, reviews and approves consequential actions.
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Enterprise AI governance
Build AI with control,
transparency and oversight.
Governance is more than a policy document. It is the practical set of roles, permissions, controls and operating practices that keep AI aligned with organizational requirements.
Permissioned action pathway
- USERIdentity & role
- AI AGENTDefined purpose
- PERMISSION LAYERAllowed actions
- ENTERPRISE SYSTEMBounded execution
- AUDIT LOGRecorded activity
“An AI agent should only be able to perform the actions it has been explicitly authorized to perform.”
The permission model should reflect the real business process, system boundaries and consequences of an action.
Actions are bounded
Grant access only to the tools and information an agent needs.
Activity is visible
Monitor interactions, exceptions and policy-relevant events.
Governance in practice
Controls across the AI lifecycle.
The right safeguards vary by use case, information sensitivity, integration surface and the actions an AI system can take. Governance should be built into discovery, engineering and operations.
Policy
AI policies
Define acceptable use, responsibilities, review and escalation expectations.
Access
Role-based access
Connect AI capabilities to appropriate user identity and access boundaries.
Data
Sensitive data
Consider how sensitive information is handled across prompts, retrieval and outputs.
People
Human oversight
Set approval, review and escalation steps where human judgment matters.
Models
Model selection
Choose models with attention to use case, performance, data and operational needs.
Evidence
Audit & monitoring
Record activity and monitor behavior, usage, exceptions and policy alignment.
Action
Agent permissions
Limit tools and actions to the permissions explicitly approved for the agent.
Risk
Risk assessment
Identify risks and controls in the context of intended use and business impact.
From principles to operating practice
Make ownership and accountability explicit.
Policies set direction. Implementation translates that direction into system access, review steps, logging, monitoring and response responsibilities.
A governance conversation includes
- Intended use, users and business ownership
- Data sensitivity and source permissions
- Agent tools, action boundaries and approvals
- Security controls, audit needs and monitoring
- Risk response and ongoing operating model
Controls are tailored to the organization and solution. This page does not claim certification or replace legal, privacy or compliance advice.
Next step
Put clear boundaries around your AI initiative.
Talk with our team about intended use, system access, oversight and the controls your organization needs to consider.
Discuss AI GovernanceExplore related capabilities